Planned tools for making pipeline development and data reproduction easier
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Updated
Sep 9, 2020 - Python
Planned tools for making pipeline development and data reproduction easier
A simple library for the implementation of the Pipeline Development pattern in .NET
Production-ready ML pipeline for telco customer churn prediction using advanced ensemble methods (XGBoost, CatBoost, Random Forest). Handles class imbalance, provides business insights, and includes modular MLOps architecture. Built with scikit-learn, featuring comprehensive EDA, feature engineering, and business impact analysis.
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